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testing for non-deterministic AI agents — behavioral contracts, adaptive sampling, GitHub Action ready","maintainers":[{"name":"bensonday","email":"bensonday89@gmail.com"}],"readme":"# AgentSpec\n\n> Regression testing for non-deterministic AI agents — behavioral contracts, adaptive sampling, GitHub Action ready.\n\n## Why?\n\nAI agents are non-deterministic. Traditional tests (`assert output == expected`) don't work. You run the same input twice and get different outputs — both might be correct.\n\nAgentSpec brings **behavioral contracts** from academic research into a practical CLI tool:\n\n- **Test behavior, not output** — \"Agent must call `search_flights` tool\" instead of \"output must equal X\"\n- **Adaptive sampling** — run 3 times instead of 30, save 70%+ token costs (based on [AgentAssay](https://arxiv.org/abs/2603.02601) research)\n- **Behavioral fingerprinting** — detect regressions even when tests pass (e.g., token usage +50%, new error paths)\n- **Statistical confidence** — \"95% confident pass rate ≥ 80%\" instead of \"passed once, ship it\"\n\n## Quick Start\n\n```bash\n# Install globally\nnpm install -g @bensonday/agent-spec\n\n# Or use npx (no install needed)\nnpx @bensonday/agent-spec init\n\n# Initialize in your project\nagentspec init\n\n# Run tests\nagentspec test\n```\n\n## Define a Contract\n\n```yaml\n# agent-spec.yaml\nagent: openai\nagentConfig:\n  model: gpt-4o\n  tools:\n    - type: function\n      function:\n        name: search_flights\n        parameters:\n          type: object\n          properties:\n            from: { type: string }\n            to: { type: string }\n            date: { type: string }\n\ncontracts:\n  - name: \"Search flights and return results\"\n    input: \"Find flights from Beijing to Shanghai tomorrow\"\n    assertions:\n      - must_call_tool: \"search_flights\"\n      - must_contain_any: [\"航班\", \"flight\", \"机票\"]\n      - must_not_error: true\n      - completes_within: \"30s\"\n      - token_budget: 5000\n    sample: 5\n    passRate: 0.8\n    adaptive: true\n```\n\n## Run Tests\n\n```bash\n# Run all contracts\nagentspec test\n\n# Filter by name\nagentspec test --filter \"search\"\n\n# Update baseline (on main branch)\nagentspec test --update\n\n# JSON output for CI\nagentspec test --json report.json\n\n# Skip regression check\nagentspec test --no-regression\n```\n\n## Real-World Examples\n\nSee [`examples/`](./examples) for complete, runnable contracts:\n\n| Example | What it tests | Key assertions |\n|---|---|---|\n| [Customer Support](./examples/customer-support/) | E-commerce bot must search KB, not over-create tickets | `must_call_tool`, `must_not_call_tool` |\n| [RAG Pipeline](./examples/rag-pipeline/) | Doc QA must cite sources, must not hallucinate | `must_contain_any`, `must_not_contain`, `token_budget` |\n| [Multi-Tool Agent](./examples/multi-tool-agent/) | Smart assistant must pick the right tool for each task | `must_call_tool` + `must_not_call_tool` combos |\n\nEach example works with **mock** (no API key) or **real API** (DeepSeek/OpenAI/Claude/Gemini).\n\n## Assertion Types\n\n| Assertion | Description |\n|---|---|\n| `must_call_tool: \"name\"` | Agent must call this tool |\n| `must_not_call_tool: \"name\"` | Agent must not call this tool |\n| `must_contain_any: [\"a\", \"b\"]` | Output must contain at least one keyword |\n| `must_contain_all: [\"a\", \"b\"]` | Output must contain all keywords |\n| `must_not_contain: [\"x\"]` | Output must not contain keywords |\n| `must_not_error: true` | Agent must not error |\n| `completes_within: \"30s\"` | Must complete within time limit |\n| `token_budget: 5000` | Token usage must not exceed budget |\n\n## GitHub Action\n\nAdd to `.github/workflows/agent-tests.yml`:\n\n```yaml\nname: Agent Regression Tests\non:\n  pull_request:\n    branches: [main]\n\njobs:\n  test:\n    runs-on: ubuntu-latest\n    steps:\n      - uses: actions/checkout@v4\n      - uses: bensonday/agent-spec@v1\n        with:\n          config: agent-spec.yaml\n          fail-on-drift: true       # fail CI on behavioral drift\n          comment-on-pr: true       # post results as PR comment\n        env:\n          DEEPSEEK_API_KEY: ${{ secrets.DEEPSEEK_API_KEY }}\n          # OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}\n          # ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}\n          # GEMINI_API_KEY: ${{ secrets.GEMINI_API_KEY }}\n```\n\n### Action Inputs\n\n| Input | Default | Description |\n|---|---|---|\n| `config` | `agent-spec.yaml` | Path to contract file |\n| `filter` | `\"\"` | Only run matching contracts |\n| `agent` | `\"\"` | Override adapter (mock/openai/claude/gemini) |\n| `update-baseline` | `false` | Update & commit baseline (use on main branch) |\n| `no-regression` | `false` | Skip regression check |\n| `fail-on-drift` | `false` | Fail CI on high-severity behavioral drift |\n| `comment-on-pr` | `false` | Post test summary as PR comment |\n\nThe Action automatically:\n- Installs AgentSpec via npm\n- Runs all contracts with adaptive sampling\n- Uploads JSON report as artifact (30-day retention)\n- Generates GitHub Actions step summary\n- Optionally comments on PR with results\n- Optionally commits baseline file on main branch\n\nSee [`examples/github-action-workflow.yml`](./examples/github-action-workflow.yml) for a complete CI setup with baseline updates.\n\n## How Adaptive Sampling Works\n\nBased on the [AgentAssay](https://arxiv.org/abs/2603.02601) paper:\n\n1. **Run 3 times** (minimum sample)\n2. **Extract behavioral fingerprints** — tool sequence, step count, token bucket, error state\n3. **If fingerprints are consistent** → behavior is deterministic, stop early (save 70%+ tokens)\n4. **If fingerprints vary** → keep sampling up to N times, compute statistical confidence\n\n### Behavioral Fingerprint Format\n\n```\nOK|search_flights,select_flight,book_flight|S3|TM|LF\n│  │                                  │  │  └─ latency bucket (Fast/Mid/Slow)\n│  │                                  │  └──── token bucket (Low/Mid/High)\n│  │                                  └─────── step count\n│  └────────────────────────────────────────── tool call sequence\n└───────────────────────────────────────────── error status (OK/ERR)\n```\n\nOnly the **pattern** is compared, not exact values — so natural variation in token count or latency won't trigger false alarms.\n\n## Agent Adapters\n\n| Adapter | Providers | Install |\n|---|---|---|\n| `mock` | Built-in, no API needed | — |\n| `openai` | OpenAI, DeepSeek, Moonshot, Qwen, 智谱GLM, MiniMax, Yi, Baichuan, SiliconFlow | `npm install openai` |\n| `claude` | Anthropic Claude | `npm install @anthropic-ai/sdk` |\n| `gemini` | Google Gemini | `npm install @google/generative-ai` |\n\n### Provider Presets\n\nUse `provider` field to auto-configure baseURL and API key for OpenAI-compatible services:\n\n```yaml\nagent: openai\nagentConfig:\n  provider: deepseek  # auto-sets baseURL + reads DEEPSEEK_API_KEY\n  # No need to manually set baseURL or apiKey\n```\n\n| Provider | Display Name | Env Var |\n|---|---|---|\n| `openai` | OpenAI | `OPENAI_API_KEY` |\n| `deepseek` | DeepSeek | `DEEPSEEK_API_KEY` |\n| `moonshot` | Moonshot (Kimi) | `MOONSHOT_API_KEY` |\n| `qwen` | 通义千问 (Qwen) | `DASHSCOPE_API_KEY` |\n| `zhipu` | 智谱 GLM | `ZHIPU_API_KEY` |\n| `minimax` | MiniMax | `MINIMAX_API_KEY` |\n| `yi` | 零一万物 (Yi) | `YI_API_KEY` |\n| `baichuan` | 百川 (Baichuan) | `BAICHUAN_API_KEY` |\n| `siliconflow` | SiliconFlow | `SILICONFLOW_API_KEY` |\n\nRun `agentspec list` to see all available adapters and providers.\n\n### Custom Adapter\n\n```typescript\nimport { AgentAdapter, AgentTrace, registerAdapter } from \"@bensonday/agent-spec\";\n\nclass MyAgent implements AgentAdapter {\n  name = \"my-agent\";\n  async run(input: string): Promise<AgentTrace> {\n    // ... your agent logic\n    return { toolsCalled, output, tokens, latency, error, steps };\n  }\n}\n\nregisterAdapter(\"my-agent\", () => new MyAgent());\n```\n\n## Programmatic API\n\n```typescript\nimport { adaptiveSample, evaluateContract, extractFingerprint } from \"@bensonday/agent-spec\";\n\n// Run a contract with adaptive sampling\nconst result = await adaptiveSample(\n  async (seed) => myAgent.run(\"Find flights\"),\n  contract,\n  { minSamples: 3, maxSamples: 10, confidenceThreshold: 0.95, passRateThreshold: 0.8, adaptive: true }\n);\n\nconsole.log(result.passRate);    // 0.9\nconsole.log(result.confidence);  // 0.55 (Wilson lower bound)\nconsole.log(result.stoppedEarly); // true (saved 70% tokens)\n```\n\n## License\n\nMIT\n","readmeFilename":"README.md"}